level stringclasses 3 values | relation_type stringlengths 1 3 | positives sequence | negatives sequence |
|---|---|---|---|
parent | 9 | [
[
"kitchen",
"cooking"
],
[
"office",
"working"
],
[
"aquarium",
"fish"
],
[
"preschool",
"kindergarten"
],
[
"glove",
"hand"
],
[
"mine",
"coal"
],
[
"kitchen",
"oven"
],
[
"winter",
"christmas"
],
[
"war",
"wea... | [
[
"mute",
"talkative"
],
[
"prisoner",
"freedom"
],
[
"sapling",
"tree"
],
[
"home",
"kitchen"
],
[
"boy",
"girl"
],
[
"spinner",
"yarn"
],
[
"kiss",
"passionate"
],
[
"fruit",
"grape"
],
[
"stirring",
"cooking"
... |
parent | 7 | [
[
"doctor",
"stethoscope"
],
[
"writer",
"pen"
],
[
"owner",
"deed"
],
[
"row",
"boat"
],
[
"instruct",
"subordinate"
],
[
"treatment",
"patient"
],
[
"absolve",
"sinner"
],
[
"preach",
"disciple"
],
[
"driver",
... | [
[
"puppy",
"dog"
],
[
"country",
"Ireland"
],
[
"map",
"route"
],
[
"furniture",
"desk"
],
[
"ladder",
"stairs"
],
[
"hot",
"cold"
],
[
"river",
"bank"
],
[
"bird",
"lips"
],
[
"gas",
"furnace"
],
[
"hat"... |
parent | 1 | [
[
"groan",
"pain"
],
[
"king",
"crown"
],
[
"fruit",
"apple"
],
[
"entomology",
"insects"
],
[
"anatomy",
"bodies"
],
[
"transportation",
"bus"
],
[
"university",
"Yale"
],
[
"utensils",
"forks"
],
[
"bird",
"rob... | [
[
"social",
"reclusive"
],
[
"lock",
"key"
],
[
"book",
"page"
],
[
"week",
"day"
],
[
"hunter",
"rifle"
],
[
"happiness",
"joy"
],
[
"omelette",
"eggs"
],
[
"earsplitting",
"quiet"
],
[
"speed",
"slowly"
],
... |
parent | 4 | [
[
"forward",
"backward"
],
[
"backward",
"forward"
],
[
"fracture",
"bone"
],
[
"young",
"old"
],
[
"dry",
"wet"
],
[
"crack",
"glass"
],
[
"deafness",
"hearing"
],
[
"stutter",
"speech"
],
[
"smooth",
"rough"
... | [
[
"child",
"adult"
],
[
"pliable",
"twisted"
],
[
"kissing",
"loving"
],
[
"tip",
"waiter"
],
[
"protect",
"vulnerable"
],
[
"foot",
"inch"
],
[
"utensil",
"spoon"
],
[
"event",
"documentary"
],
[
"road",
"highwa... |
parent | 5 | [
[
"fragile",
"damaged"
],
[
"glue",
"sticky"
],
[
"candy",
"sweet"
],
[
"sad",
"depression"
],
[
"thaw",
"frozen"
],
[
"eat",
"edible"
],
[
"tycoon",
"wealthy"
],
[
"fire",
"hot"
],
[
"joke",
"laughter"
],
[
... | [
[
"sleep",
"rest"
],
[
"investor",
"investments"
],
[
"exhausted",
"run"
],
[
"mother",
"children"
],
[
"pacifist",
"fight"
],
[
"morning",
"sunrise"
],
[
"thirst",
"drink"
],
[
"mask",
"face"
],
[
"warn",
"threa... |
parent | 3 | [
[
"big",
"large"
],
[
"hunger",
"starvation"
],
[
"negotiate",
"blackmail"
],
[
"make",
"manufacture"
],
[
"man",
"woman"
],
[
"stairs",
"ladder"
],
[
"pencil",
"pen"
],
[
"inflation",
"price"
],
[
"cute",
"adora... | [
[
"blacksmith",
"iron"
],
[
"progress",
"regress"
],
[
"oil",
"lamp"
],
[
"winter",
"sledding"
],
[
"loss",
"grief"
],
[
"script",
"movie"
],
[
"sunglasses",
"eyes"
],
[
"destitution",
"abundance"
],
[
"liar",
"h... |
parent | 8 | [
[
"moisturize",
"dryness"
],
[
"sadness",
"cry"
],
[
"food",
"nourishment"
],
[
"fungicide",
"fungus"
],
[
"tiredness",
"rest"
],
[
"athlete",
"championship"
],
[
"tylenol",
"relief"
],
[
"electricity",
"lamp"
],
[
"... | [
[
"knife",
"sword"
],
[
"game",
"referee"
],
[
"owner",
"deed"
],
[
"retiree",
"job"
],
[
"bracelet",
"wrist"
],
[
"flower",
"daisy"
],
[
"selfish",
"donate"
],
[
"bird",
"lips"
],
[
"lion",
"roar"
],
[
"... |
parent | 6 | [
[
"fire",
"cold"
],
[
"groom",
"unkempt"
],
[
"mute",
"talkative"
],
[
"drought",
"water"
],
[
"hurry",
"lazily"
],
[
"optimist",
"despair"
],
[
"arrogant",
"humble"
],
[
"whisper",
"loudly"
],
[
"hero",
"cower"
... | [
[
"good",
"wrong"
],
[
"powerful",
"meek"
],
[
"contract",
"deal"
],
[
"queen",
"throne"
],
[
"fragile",
"unbreakable"
],
[
"tear",
"sadness"
],
[
"city",
"Dallas"
],
[
"color",
"green"
],
[
"smoke",
"fire"
],
... |
parent | 10 | [
[
"biology",
"life"
],
[
"map",
"route"
],
[
"zoology",
"animals"
],
[
"life",
"biography"
],
[
"tear",
"sadness"
],
[
"diploma",
"education"
],
[
"image",
"photograph"
],
[
"crown",
"royalty"
],
[
"laugh",
"happ... | [
[
"foot",
"toe"
],
[
"run",
"fast"
],
[
"clean",
"messy"
],
[
"evil",
"angelic"
],
[
"narrative",
"epilogue"
],
[
"telephone",
"call"
],
[
"full",
"empty"
],
[
"omelette",
"eggs"
],
[
"permanent",
"changed"
],
... |
parent | 2 | [
[
"husband",
"bachelorhood"
],
[
"ocean",
"dryness"
],
[
"hill",
"top"
],
[
"forest",
"tree"
],
[
"clothing",
"thread"
],
[
"unemployed",
"job"
],
[
"pound",
"ounce"
],
[
"week",
"day"
],
[
"building",
"lobby"
... | [
[
"quadriplegic",
"walk"
],
[
"wash",
"clean"
],
[
"immunization",
"disease"
],
[
"fragile",
"damaged"
],
[
"designer",
"fashions"
],
[
"prince",
"Charles"
],
[
"winter",
"christmas"
],
[
"birds",
"crows"
],
[
"empty... |
child | 4a | [
[
"cooked",
"raw"
],
[
"fat",
"thin"
],
[
"smooth",
"rough"
],
[
"noise",
"silence"
],
[
"young",
"old"
],
[
"rich",
"poor"
],
[
"happy",
"sad"
],
[
"dry",
"wet"
]
] | [
[
"autumn",
"halloween"
],
[
"scar",
"beauty"
],
[
"wash",
"dirty"
],
[
"bird",
"feathers"
],
[
"novice",
"experience"
],
[
"automobile",
"transportation"
],
[
"thoughtful",
"contemplation"
],
[
"christmas",
"presents"
],
... |
child | 5e | [
[
"chef",
"cook"
],
[
"pastor",
"preach"
],
[
"heart",
"beat"
],
[
"dynamite",
"explode"
],
[
"dog",
"bark"
],
[
"tailor",
"sew"
],
[
"boxer",
"fight"
],
[
"fire",
"burn"
]
] | [
[
"award",
"winner"
],
[
"hat",
"hair"
],
[
"transform",
"malleable"
],
[
"mixing",
"baking"
],
[
"summer",
"vacation"
],
[
"fork",
"eat"
],
[
"lower",
"volume"
],
[
"concert",
"musician"
],
[
"grimace",
"disgust... |
End of preview. Expand
in Data Studio
Dataset Card for "relbert/semeval2012_relational_similarity_v4"
Dataset Summary
IMPORTANT: This is the same dataset as relbert/semeval2012_relational_similarity, but with a different dataset construction.
Relational similarity dataset from SemEval2012 task 2, compiled to fine-tune RelBERT model. The dataset contains a list of positive and negative word pair from 89 pre-defined relations. The relation types are constructed on top of following 10 parent relation types.
{
1: "Class Inclusion", # Hypernym
2: "Part-Whole", # Meronym, Substance Meronym
3: "Similar", # Synonym, Co-hypornym
4: "Contrast", # Antonym
5: "Attribute", # Attribute, Event
6: "Non Attribute",
7: "Case Relation",
8: "Cause-Purpose",
9: "Space-Time",
10: "Representation"
}
Each of the parent relation is further grouped into child relation types where the definition can be found here.
Dataset Structure
Data Instances
An example of train looks as follows.
{
'relation_type': '8d',
'positives': [ [ "breathe", "live" ], [ "study", "learn" ], [ "speak", "communicate" ], ... ]
'negatives': [ [ "starving", "hungry" ], [ "clean", "bathe" ], [ "hungry", "starving" ], ... ]
}
Data Splits
| name | train | validation |
|---|---|---|
| semeval2012_relational_similarity | 89 | 89 |
Number of Positive/Negative Word-pairs in each Split
| positives | negatives | |
|---|---|---|
| ('1', 'parent', 'train') | 88 | 544 |
| ('1', 'parent', 'validation') | 22 | 136 |
| ('10', 'parent', 'train') | 48 | 584 |
| ('10', 'parent', 'validation') | 12 | 146 |
| ('10a', 'child', 'train') | 8 | 1324 |
| ('10a', 'child', 'validation') | 2 | 331 |
| ('10a', 'child_prototypical', 'train') | 97 | 1917 |
| ('10a', 'child_prototypical', 'validation') | 26 | 521 |
| ('10b', 'child', 'train') | 8 | 1325 |
| ('10b', 'child', 'validation') | 2 | 331 |
| ('10b', 'child_prototypical', 'train') | 90 | 1558 |
| ('10b', 'child_prototypical', 'validation') | 27 | 469 |
| ('10c', 'child', 'train') | 8 | 1327 |
| ('10c', 'child', 'validation') | 2 | 331 |
| ('10c', 'child_prototypical', 'train') | 85 | 1640 |
| ('10c', 'child_prototypical', 'validation') | 20 | 390 |
| ('10d', 'child', 'train') | 8 | 1328 |
| ('10d', 'child', 'validation') | 2 | 331 |
| ('10d', 'child_prototypical', 'train') | 77 | 1390 |
| ('10d', 'child_prototypical', 'validation') | 22 | 376 |
| ('10e', 'child', 'train') | 8 | 1329 |
| ('10e', 'child', 'validation') | 2 | 332 |
| ('10e', 'child_prototypical', 'train') | 67 | 884 |
| ('10e', 'child_prototypical', 'validation') | 20 | 234 |
| ('10f', 'child', 'train') | 8 | 1328 |
| ('10f', 'child', 'validation') | 2 | 331 |
| ('10f', 'child_prototypical', 'train') | 80 | 1460 |
| ('10f', 'child_prototypical', 'validation') | 19 | 306 |
| ('1a', 'child', 'train') | 8 | 1324 |
| ('1a', 'child', 'validation') | 2 | 331 |
| ('1a', 'child_prototypical', 'train') | 106 | 1854 |
| ('1a', 'child_prototypical', 'validation') | 17 | 338 |
| ('1b', 'child', 'train') | 8 | 1324 |
| ('1b', 'child', 'validation') | 2 | 331 |
| ('1b', 'child_prototypical', 'train') | 95 | 1712 |
| ('1b', 'child_prototypical', 'validation') | 28 | 480 |
| ('1c', 'child', 'train') | 8 | 1327 |
| ('1c', 'child', 'validation') | 2 | 331 |
| ('1c', 'child_prototypical', 'train') | 80 | 1528 |
| ('1c', 'child_prototypical', 'validation') | 25 | 502 |
| ('1d', 'child', 'train') | 8 | 1323 |
| ('1d', 'child', 'validation') | 2 | 330 |
| ('1d', 'child_prototypical', 'train') | 112 | 2082 |
| ('1d', 'child_prototypical', 'validation') | 23 | 458 |
| ('1e', 'child', 'train') | 8 | 1329 |
| ('1e', 'child', 'validation') | 2 | 332 |
| ('1e', 'child_prototypical', 'train') | 63 | 775 |
| ('1e', 'child_prototypical', 'validation') | 24 | 256 |
| ('2', 'parent', 'train') | 80 | 552 |
| ('2', 'parent', 'validation') | 20 | 138 |
| ('2a', 'child', 'train') | 8 | 1324 |
| ('2a', 'child', 'validation') | 2 | 330 |
| ('2a', 'child_prototypical', 'train') | 93 | 1885 |
| ('2a', 'child_prototypical', 'validation') | 36 | 736 |
| ('2b', 'child', 'train') | 8 | 1327 |
| ('2b', 'child', 'validation') | 2 | 331 |
| ('2b', 'child_prototypical', 'train') | 86 | 1326 |
| ('2b', 'child_prototypical', 'validation') | 19 | 284 |
| ('2c', 'child', 'train') | 8 | 1325 |
| ('2c', 'child', 'validation') | 2 | 331 |
| ('2c', 'child_prototypical', 'train') | 96 | 1773 |
| ('2c', 'child_prototypical', 'validation') | 21 | 371 |
| ('2d', 'child', 'train') | 8 | 1328 |
| ('2d', 'child', 'validation') | 2 | 331 |
| ('2d', 'child_prototypical', 'train') | 79 | 1329 |
| ('2d', 'child_prototypical', 'validation') | 20 | 338 |
| ('2e', 'child', 'train') | 8 | 1327 |
| ('2e', 'child', 'validation') | 2 | 331 |
| ('2e', 'child_prototypical', 'train') | 82 | 1462 |
| ('2e', 'child_prototypical', 'validation') | 23 | 463 |
| ('2f', 'child', 'train') | 8 | 1327 |
| ('2f', 'child', 'validation') | 2 | 331 |
| ('2f', 'child_prototypical', 'train') | 88 | 1869 |
| ('2f', 'child_prototypical', 'validation') | 17 | 371 |
| ('2g', 'child', 'train') | 8 | 1323 |
| ('2g', 'child', 'validation') | 2 | 330 |
| ('2g', 'child_prototypical', 'train') | 108 | 1925 |
| ('2g', 'child_prototypical', 'validation') | 27 | 480 |
| ('2h', 'child', 'train') | 8 | 1327 |
| ('2h', 'child', 'validation') | 2 | 331 |
| ('2h', 'child_prototypical', 'train') | 84 | 1540 |
| ('2h', 'child_prototypical', 'validation') | 21 | 385 |
| ('2i', 'child', 'train') | 8 | 1328 |
| ('2i', 'child', 'validation') | 2 | 332 |
| ('2i', 'child_prototypical', 'train') | 72 | 1335 |
| ('2i', 'child_prototypical', 'validation') | 21 | 371 |
| ('2j', 'child', 'train') | 8 | 1328 |
| ('2j', 'child', 'validation') | 2 | 331 |
| ('2j', 'child_prototypical', 'train') | 80 | 1595 |
| ('2j', 'child_prototypical', 'validation') | 19 | 369 |
| ('3', 'parent', 'train') | 64 | 568 |
| ('3', 'parent', 'validation') | 16 | 142 |
| ('3a', 'child', 'train') | 8 | 1327 |
| ('3a', 'child', 'validation') | 2 | 331 |
| ('3a', 'child_prototypical', 'train') | 87 | 1597 |
| ('3a', 'child_prototypical', 'validation') | 18 | 328 |
| ('3b', 'child', 'train') | 8 | 1327 |
| ('3b', 'child', 'validation') | 2 | 331 |
| ('3b', 'child_prototypical', 'train') | 87 | 1833 |
| ('3b', 'child_prototypical', 'validation') | 18 | 407 |
| ('3c', 'child', 'train') | 8 | 1326 |
| ('3c', 'child', 'validation') | 2 | 331 |
| ('3c', 'child_prototypical', 'train') | 93 | 1664 |
| ('3c', 'child_prototypical', 'validation') | 18 | 315 |
| ('3d', 'child', 'train') | 8 | 1324 |
| ('3d', 'child', 'validation') | 2 | 331 |
| ('3d', 'child_prototypical', 'train') | 101 | 1943 |
| ('3d', 'child_prototypical', 'validation') | 22 | 372 |
| ('3e', 'child', 'train') | 8 | 1332 |
| ('3e', 'child', 'validation') | 2 | 332 |
| ('3e', 'child_prototypical', 'train') | 49 | 900 |
| ('3e', 'child_prototypical', 'validation') | 20 | 368 |
| ('3f', 'child', 'train') | 8 | 1327 |
| ('3f', 'child', 'validation') | 2 | 331 |
| ('3f', 'child_prototypical', 'train') | 90 | 1983 |
| ('3f', 'child_prototypical', 'validation') | 15 | 362 |
| ('3g', 'child', 'train') | 8 | 1331 |
| ('3g', 'child', 'validation') | 2 | 332 |
| ('3g', 'child_prototypical', 'train') | 61 | 1089 |
| ('3g', 'child_prototypical', 'validation') | 14 | 251 |
| ('3h', 'child', 'train') | 8 | 1328 |
| ('3h', 'child', 'validation') | 2 | 331 |
| ('3h', 'child_prototypical', 'train') | 71 | 1399 |
| ('3h', 'child_prototypical', 'validation') | 28 | 565 |
| ('4', 'parent', 'train') | 64 | 568 |
| ('4', 'parent', 'validation') | 16 | 142 |
| ('4a', 'child', 'train') | 8 | 1327 |
| ('4a', 'child', 'validation') | 2 | 331 |
| ('4a', 'child_prototypical', 'train') | 85 | 1766 |
| ('4a', 'child_prototypical', 'validation') | 20 | 474 |
| ('4b', 'child', 'train') | 8 | 1330 |
| ('4b', 'child', 'validation') | 2 | 332 |
| ('4b', 'child_prototypical', 'train') | 66 | 949 |
| ('4b', 'child_prototypical', 'validation') | 15 | 214 |
| ('4c', 'child', 'train') | 8 | 1326 |
| ('4c', 'child', 'validation') | 2 | 331 |
| ('4c', 'child_prototypical', 'train') | 86 | 1755 |
| ('4c', 'child_prototypical', 'validation') | 25 | 446 |
| ('4d', 'child', 'train') | 8 | 1332 |
| ('4d', 'child', 'validation') | 2 | 333 |
| ('4d', 'child_prototypical', 'train') | 46 | 531 |
| ('4d', 'child_prototypical', 'validation') | 17 | 218 |
| ('4e', 'child', 'train') | 8 | 1326 |
| ('4e', 'child', 'validation') | 2 | 331 |
| ('4e', 'child_prototypical', 'train') | 92 | 2021 |
| ('4e', 'child_prototypical', 'validation') | 19 | 402 |
| ('4f', 'child', 'train') | 8 | 1328 |
| ('4f', 'child', 'validation') | 2 | 332 |
| ('4f', 'child_prototypical', 'train') | 72 | 1464 |
| ('4f', 'child_prototypical', 'validation') | 21 | 428 |
| ('4g', 'child', 'train') | 8 | 1324 |
| ('4g', 'child', 'validation') | 2 | 330 |
| ('4g', 'child_prototypical', 'train') | 106 | 2057 |
| ('4g', 'child_prototypical', 'validation') | 23 | 435 |
| ('4h', 'child', 'train') | 8 | 1326 |
| ('4h', 'child', 'validation') | 2 | 331 |
| ('4h', 'child_prototypical', 'train') | 85 | 1787 |
| ('4h', 'child_prototypical', 'validation') | 26 | 525 |
| ('5', 'parent', 'train') | 72 | 560 |
| ('5', 'parent', 'validation') | 18 | 140 |
| ('5a', 'child', 'train') | 8 | 1324 |
| ('5a', 'child', 'validation') | 2 | 331 |
| ('5a', 'child_prototypical', 'train') | 101 | 1876 |
| ('5a', 'child_prototypical', 'validation') | 22 | 439 |
| ('5b', 'child', 'train') | 8 | 1329 |
| ('5b', 'child', 'validation') | 2 | 332 |
| ('5b', 'child_prototypical', 'train') | 70 | 1310 |
| ('5b', 'child_prototypical', 'validation') | 17 | 330 |
| ('5c', 'child', 'train') | 8 | 1327 |
| ('5c', 'child', 'validation') | 2 | 331 |
| ('5c', 'child_prototypical', 'train') | 85 | 1552 |
| ('5c', 'child_prototypical', 'validation') | 20 | 373 |
| ('5d', 'child', 'train') | 8 | 1324 |
| ('5d', 'child', 'validation') | 2 | 330 |
| ('5d', 'child_prototypical', 'train') | 102 | 1783 |
| ('5d', 'child_prototypical', 'validation') | 27 | 580 |
| ('5e', 'child', 'train') | 8 | 1329 |
| ('5e', 'child', 'validation') | 2 | 332 |
| ('5e', 'child_prototypical', 'train') | 68 | 1283 |
| ('5e', 'child_prototypical', 'validation') | 19 | 357 |
| ('5f', 'child', 'train') | 8 | 1327 |
| ('5f', 'child', 'validation') | 2 | 331 |
| ('5f', 'child_prototypical', 'train') | 77 | 1568 |
| ('5f', 'child_prototypical', 'validation') | 28 | 567 |
| ('5g', 'child', 'train') | 8 | 1328 |
| ('5g', 'child', 'validation') | 2 | 332 |
| ('5g', 'child_prototypical', 'train') | 79 | 1626 |
| ('5g', 'child_prototypical', 'validation') | 14 | 266 |
| ('5h', 'child', 'train') | 8 | 1324 |
| ('5h', 'child', 'validation') | 2 | 330 |
| ('5h', 'child_prototypical', 'train') | 109 | 2348 |
| ('5h', 'child_prototypical', 'validation') | 20 | 402 |
| ('5i', 'child', 'train') | 8 | 1324 |
| ('5i', 'child', 'validation') | 2 | 331 |
| ('5i', 'child_prototypical', 'train') | 96 | 2010 |
| ('5i', 'child_prototypical', 'validation') | 27 | 551 |
| ('6', 'parent', 'train') | 64 | 568 |
| ('6', 'parent', 'validation') | 16 | 142 |
| ('6a', 'child', 'train') | 8 | 1324 |
| ('6a', 'child', 'validation') | 2 | 330 |
| ('6a', 'child_prototypical', 'train') | 102 | 1962 |
| ('6a', 'child_prototypical', 'validation') | 27 | 530 |
| ('6b', 'child', 'train') | 8 | 1327 |
| ('6b', 'child', 'validation') | 2 | 331 |
| ('6b', 'child_prototypical', 'train') | 90 | 1840 |
| ('6b', 'child_prototypical', 'validation') | 15 | 295 |
| ('6c', 'child', 'train') | 8 | 1325 |
| ('6c', 'child', 'validation') | 2 | 331 |
| ('6c', 'child_prototypical', 'train') | 90 | 1968 |
| ('6c', 'child_prototypical', 'validation') | 27 | 527 |
| ('6d', 'child', 'train') | 8 | 1328 |
| ('6d', 'child', 'validation') | 2 | 331 |
| ('6d', 'child_prototypical', 'train') | 82 | 1903 |
| ('6d', 'child_prototypical', 'validation') | 17 | 358 |
| ('6e', 'child', 'train') | 8 | 1327 |
| ('6e', 'child', 'validation') | 2 | 331 |
| ('6e', 'child_prototypical', 'train') | 85 | 1737 |
| ('6e', 'child_prototypical', 'validation') | 20 | 398 |
| ('6f', 'child', 'train') | 8 | 1326 |
| ('6f', 'child', 'validation') | 2 | 331 |
| ('6f', 'child_prototypical', 'train') | 87 | 1652 |
| ('6f', 'child_prototypical', 'validation') | 24 | 438 |
| ('6g', 'child', 'train') | 8 | 1326 |
| ('6g', 'child', 'validation') | 2 | 331 |
| ('6g', 'child_prototypical', 'train') | 94 | 1740 |
| ('6g', 'child_prototypical', 'validation') | 17 | 239 |
| ('6h', 'child', 'train') | 8 | 1324 |
| ('6h', 'child', 'validation') | 2 | 330 |
| ('6h', 'child_prototypical', 'train') | 115 | 2337 |
| ('6h', 'child_prototypical', 'validation') | 14 | 284 |
| ('7', 'parent', 'train') | 64 | 568 |
| ('7', 'parent', 'validation') | 16 | 142 |
| ('7a', 'child', 'train') | 8 | 1324 |
| ('7a', 'child', 'validation') | 2 | 331 |
| ('7a', 'child_prototypical', 'train') | 99 | 2045 |
| ('7a', 'child_prototypical', 'validation') | 24 | 516 |
| ('7b', 'child', 'train') | 8 | 1330 |
| ('7b', 'child', 'validation') | 2 | 332 |
| ('7b', 'child_prototypical', 'train') | 69 | 905 |
| ('7b', 'child_prototypical', 'validation') | 12 | 177 |
| ('7c', 'child', 'train') | 8 | 1327 |
| ('7c', 'child', 'validation') | 2 | 331 |
| ('7c', 'child_prototypical', 'train') | 85 | 1402 |
| ('7c', 'child_prototypical', 'validation') | 20 | 313 |
| ('7d', 'child', 'train') | 8 | 1324 |
| ('7d', 'child', 'validation') | 2 | 331 |
| ('7d', 'child_prototypical', 'train') | 98 | 2064 |
| ('7d', 'child_prototypical', 'validation') | 25 | 497 |
| ('7e', 'child', 'train') | 8 | 1328 |
| ('7e', 'child', 'validation') | 2 | 331 |
| ('7e', 'child_prototypical', 'train') | 78 | 1270 |
| ('7e', 'child_prototypical', 'validation') | 21 | 298 |
| ('7f', 'child', 'train') | 8 | 1326 |
| ('7f', 'child', 'validation') | 2 | 331 |
| ('7f', 'child_prototypical', 'train') | 89 | 1377 |
| ('7f', 'child_prototypical', 'validation') | 22 | 380 |
| ('7g', 'child', 'train') | 8 | 1328 |
| ('7g', 'child', 'validation') | 2 | 332 |
| ('7g', 'child_prototypical', 'train') | 72 | 885 |
| ('7g', 'child_prototypical', 'validation') | 21 | 263 |
| ('7h', 'child', 'train') | 8 | 1324 |
| ('7h', 'child', 'validation') | 2 | 331 |
| ('7h', 'child_prototypical', 'train') | 94 | 1479 |
| ('7h', 'child_prototypical', 'validation') | 29 | 467 |
| ('8', 'parent', 'train') | 64 | 568 |
| ('8', 'parent', 'validation') | 16 | 142 |
| ('8a', 'child', 'train') | 8 | 1324 |
| ('8a', 'child', 'validation') | 2 | 331 |
| ('8a', 'child_prototypical', 'train') | 93 | 1640 |
| ('8a', 'child_prototypical', 'validation') | 30 | 552 |
| ('8b', 'child', 'train') | 8 | 1330 |
| ('8b', 'child', 'validation') | 2 | 332 |
| ('8b', 'child_prototypical', 'train') | 61 | 1126 |
| ('8b', 'child_prototypical', 'validation') | 20 | 361 |
| ('8c', 'child', 'train') | 8 | 1326 |
| ('8c', 'child', 'validation') | 2 | 331 |
| ('8c', 'child_prototypical', 'train') | 96 | 1547 |
| ('8c', 'child_prototypical', 'validation') | 15 | 210 |
| ('8d', 'child', 'train') | 8 | 1325 |
| ('8d', 'child', 'validation') | 2 | 331 |
| ('8d', 'child_prototypical', 'train') | 92 | 1472 |
| ('8d', 'child_prototypical', 'validation') | 25 | 438 |
| ('8e', 'child', 'train') | 8 | 1327 |
| ('8e', 'child', 'validation') | 2 | 331 |
| ('8e', 'child_prototypical', 'train') | 87 | 1340 |
| ('8e', 'child_prototypical', 'validation') | 18 | 270 |
| ('8f', 'child', 'train') | 8 | 1326 |
| ('8f', 'child', 'validation') | 2 | 331 |
| ('8f', 'child_prototypical', 'train') | 83 | 1416 |
| ('8f', 'child_prototypical', 'validation') | 28 | 452 |
| ('8g', 'child', 'train') | 8 | 1330 |
| ('8g', 'child', 'validation') | 2 | 332 |
| ('8g', 'child_prototypical', 'train') | 62 | 640 |
| ('8g', 'child_prototypical', 'validation') | 19 | 199 |
| ('8h', 'child', 'train') | 8 | 1324 |
| ('8h', 'child', 'validation') | 2 | 331 |
| ('8h', 'child_prototypical', 'train') | 100 | 1816 |
| ('8h', 'child_prototypical', 'validation') | 23 | 499 |
| ('9', 'parent', 'train') | 72 | 560 |
| ('9', 'parent', 'validation') | 18 | 140 |
| ('9a', 'child', 'train') | 8 | 1324 |
| ('9a', 'child', 'validation') | 2 | 331 |
| ('9a', 'child_prototypical', 'train') | 96 | 1520 |
| ('9a', 'child_prototypical', 'validation') | 27 | 426 |
| ('9b', 'child', 'train') | 8 | 1326 |
| ('9b', 'child', 'validation') | 2 | 331 |
| ('9b', 'child_prototypical', 'train') | 93 | 1783 |
| ('9b', 'child_prototypical', 'validation') | 18 | 307 |
| ('9c', 'child', 'train') | 8 | 1330 |
| ('9c', 'child', 'validation') | 2 | 332 |
| ('9c', 'child_prototypical', 'train') | 59 | 433 |
| ('9c', 'child_prototypical', 'validation') | 22 | 163 |
| ('9d', 'child', 'train') | 8 | 1328 |
| ('9d', 'child', 'validation') | 2 | 332 |
| ('9d', 'child_prototypical', 'train') | 78 | 1683 |
| ('9d', 'child_prototypical', 'validation') | 15 | 302 |
| ('9e', 'child', 'train') | 8 | 1329 |
| ('9e', 'child', 'validation') | 2 | 332 |
| ('9e', 'child_prototypical', 'train') | 66 | 1426 |
| ('9e', 'child_prototypical', 'validation') | 21 | 475 |
| ('9f', 'child', 'train') | 8 | 1328 |
| ('9f', 'child', 'validation') | 2 | 331 |
| ('9f', 'child_prototypical', 'train') | 79 | 1436 |
| ('9f', 'child_prototypical', 'validation') | 20 | 330 |
| ('9g', 'child', 'train') | 8 | 1324 |
| ('9g', 'child', 'validation') | 2 | 331 |
| ('9g', 'child_prototypical', 'train') | 100 | 1685 |
| ('9g', 'child_prototypical', 'validation') | 23 | 384 |
| ('9h', 'child', 'train') | 8 | 1325 |
| ('9h', 'child', 'validation') | 2 | 331 |
| ('9h', 'child_prototypical', 'train') | 95 | 1799 |
| ('9h', 'child_prototypical', 'validation') | 22 | 462 |
| ('9i', 'child', 'train') | 8 | 1328 |
| ('9i', 'child', 'validation') | 2 | 332 |
| ('9i', 'child_prototypical', 'train') | 79 | 1361 |
| ('9i', 'child_prototypical', 'validation') | 14 | 252 |
Citation Information
@inproceedings{jurgens-etal-2012-semeval,
title = "{S}em{E}val-2012 Task 2: Measuring Degrees of Relational Similarity",
author = "Jurgens, David and
Mohammad, Saif and
Turney, Peter and
Holyoak, Keith",
booktitle = "*{SEM} 2012: The First Joint Conference on Lexical and Computational Semantics {--} Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation ({S}em{E}val 2012)",
month = "7-8 " # jun,
year = "2012",
address = "Montr{\'e}al, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S12-1047",
pages = "356--364",
}
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